{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": true
    }
   },
   "source": [
    "**HyperTS supports univariate and multivariate time series anomaly detection tasks.**\n",
    "\n",
    "**In this NoteBook, we will use HyperTS to do a time series anomaly detection task.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Step 1: Let`s still use **realKnownCause** dataset, import it and do EDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperts.datasets import load_real_known_cause_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = load_real_known_cause_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-02 21:15:00</td>\n",
       "      <td>73.967322</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-12-02 21:20:00</td>\n",
       "      <td>74.935882</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-12-02 21:25:00</td>\n",
       "      <td>76.124162</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-02 21:30:00</td>\n",
       "      <td>78.140707</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-02 21:35:00</td>\n",
       "      <td>79.329836</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp      value  anomaly\n",
       "0  2013-12-02 21:15:00  73.967322        0\n",
       "1  2013-12-02 21:20:00  74.935882        0\n",
       "2  2013-12-02 21:25:00  76.124162        0\n",
       "3  2013-12-02 21:30:00  78.140707        0\n",
       "4  2013-12-02 21:35:00  79.329836        0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table, we can see that:\n",
    "\n",
    "- timestamp column name: 'timestamp';\n",
    "- feature column name: 'value';\n",
    "- label column name: 'anomaly';\n",
    "- time freq: '5T';\n",
    "\n",
    "The anomaly detection scenario is usually unlabeled, so we remove the label column and transform it into an unsuperised task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ground_truth = df.pop('anomaly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22683 entries, 0 to 22682\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   timestamp  22683 non-null  object \n",
      " 1   value      22683 non-null  float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 354.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split train_data and test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperts.toolbox import temporal_train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = temporal_train_test_split(df, test_horizon=15000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visulization of train_data curve and test_data curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers = ground_truth.loc[ground_truth.values == 1]\n",
    "outliers_index = list(outliers.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.plot(pd.to_datetime(train_data['timestamp']), \n",
    "         train_data['value'], \n",
    "         c='gray', \n",
    "         label='train data')\n",
    "plt.plot(pd.to_datetime(test_data['timestamp']), \n",
    "         test_data['value'], \n",
    "         c='red', \n",
    "         label='test data')\n",
    "plt.scatter(pd.to_datetime(df.loc[outliers_index, 'timestamp']), \n",
    "            df.loc[outliers_index, 'value'], \n",
    "            c='blue',\n",
    "            marker='x',\n",
    "            label='anomaly')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Step 2: Create experiments and search for models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure some parameters for experiment. For details, see make_experiment comments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperts import make_experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "experiment = make_experiment(\n",
    "    train_data=train_data.copy(),\n",
    "    mode='stats',\n",
    "    task='detection',\n",
    "    timestamp='timestamp',\n",
    "    max_trials=30,\n",
    "    random_state=2022)\n",
    "\n",
    "model = experiment.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Gets pipeline model parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Pipeline.get_params of Pipeline(steps=[('data_preprocessing',\n",
       "                 TSFDataPreprocessStep(covariate_cleaner=IdentityTransformer(),\n",
       "                                       ensemble_size=10, freq='5T',\n",
       "                                       name='data_preprocessing',\n",
       "                                       timestamp_col=['timestamp'])),\n",
       "                ('estimator',\n",
       "                 TSGreedyEnsemble(weight=[0.4, 0.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], scores=[0.7789473684210527, 0.7789473684210527, 0.7789473684210527, 0.7789473684210527, 0.7789473684210527, 0.7789473684210527, 0.7789473684210527, 0.7789473684210527, 0.7789473684210527, 0.7789473684210527]))])>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_pipeline_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Detection on test data (unknown data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, _ = model.split_X_y(test_data.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-29 13:30:00</td>\n",
       "      <td>82.032343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-12-29 13:35:00</td>\n",
       "      <td>80.900366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-12-29 13:40:00</td>\n",
       "      <td>81.031536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-29 13:45:00</td>\n",
       "      <td>80.200337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-29 13:50:00</td>\n",
       "      <td>80.182448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp      value\n",
       "0  2013-12-29 13:30:00  82.032343\n",
       "1  2013-12-29 13:35:00  80.900366\n",
       "2  2013-12-29 13:40:00  81.031536\n",
       "3  2013-12-29 13:45:00  80.200337\n",
       "4  2013-12-29 13:50:00  80.182448"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_proba = model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>anomaly</th>\n",
       "      <th>severity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-29 13:30:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.138589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-12-29 13:35:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.178984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-12-29 13:40:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.162511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-29 13:45:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.203910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-29 13:50:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.202604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp  anomaly  severity\n",
       "0 2013-12-29 13:30:00        0  0.138589\n",
       "1 2013-12-29 13:35:00        0  0.178984\n",
       "2 2013-12-29 13:40:00        0  0.162511\n",
       "3 2013-12-29 13:45:00        0  0.203910\n",
       "4 2013-12-29 13:50:00        0  0.202604"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `predict` of HyperTS'model will output a DataFrame containing the **anomaly** and **severity** for each timestamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = ground_truth.iloc[-15000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = model.evaluate(y_true=y_test, y_pred=y_pred, y_proba=y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metirc</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.9480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f1</td>\n",
       "      <td>0.5933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>precision</td>\n",
       "      <td>0.7258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>recall</td>\n",
       "      <td>0.5018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Metirc  Score\n",
       "0   accuracy 0.9480\n",
       "1         f1 0.5933\n",
       "2  precision 0.7258\n",
       "3     recall 0.5018"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step6: Visualize the detection curve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HyperTS supports two ways to draw forecast curves: interactive (plotly installation required) and non-interactive (matplotlib installation required). The default is interactive. If you want to draw non-interactive, you can set ```interactive=False```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot(y_pred, actual=test_data, history=train_data, interactive=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
